System and method for image correction

ABSTRACT

A system and method for image correction is provided. The method includes: receiving an original image; obtaining an image relating to a region of interest (ROI); detecting an artifact in the image relating to the ROI; generating an artifact image based on the artifact; and correcting the original image based on the artifact image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of a U.S. application Ser. No.16/729,515 filed on Dec. 30, 2019, which is divisional application ofU.S. application Ser. No. 15/323,062 (issued as U.S. Pat. No.10,521,886) filed on Dec. 29, 2016, which is a U.S. national stage under35 U.S.C. § 371 of International Application No. PCT/CN2016/102005 filedon Oct. 13, 2016, designating the United States of America, which claimspriority of Chinese Patent Application No. 201510660920.0 filed Oct. 14,2015 and Chinese Patent Application No. 201610038856.7 filed Jan. 21,2016, the contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

The present disclosure generally relates to imaging, and moreparticularly to a system and method for image correction in computedtomography.

BACKGROUND

Computed tomography (CT) is a technology that makes use ofcomputer-processed combinations of X-ray images taken from differentangles to produce cross-sectional images. The CT technology has beenwidely used in medical diagnosis. During a process of a CT scanning,artifacts including metal artifacts or streak artifacts may occur. Theartifacts may reduce the image quality and influence the diagnosisresults. There is a need for a system and method to remove the artifactsfrom the images and improve the image quality.

SUMMARY

In a first aspect of the present disclosure, a method for imagecorrection is provided. The method may include one or more of thefollowing operations. An original image may be received. The originalimage may be pre-corrected. Correction data may be generated based onthe original image and the pre-corrected image. The original image andthe pre-corrected image may be weighted based on the correction data. Acorrected image may be generated based on the weighting.

In some embodiments, the correction data may include projection data ofthe original image, projection data of the pre-corrected image, or anerror image of the original image.

In some embodiments, a metal image including metal information may bedetermined based on the original image. Projection data of the artifactimage may be generated. An interpolation may be performed based on theprojection data of the artifact image and the projection data of theoriginal image.

In some embodiments, the projection data of the original image and theprojection data of the pre-corrected image may be weighted.

In some embodiments, the weighting the original image and thepre-corrected image may be performed based on a regularity degree of theartifact.

In some embodiments, when the regularity degree is below a firstthreshold, the weighting may be performed according to a first weightingintensity; when the regularity degree exceeds the first threshold butbelow a second threshold, the weighting may be performed according to asecond weighting intensity; and when the regularity degree exceeds thesecond threshold, the weighting may be performed according to a thirdweighting intensity.

In some embodiments, the error image may be generated based on thepre-corrected image and the original image.

In some embodiments, a plurality of weighting coefficients may bedetermined. A weighting coefficient may correspond to a pixel in theerror image. The original image and the error image may be weightedbased on the plurality of weighting coefficients.

In some embodiments, the information entropy of a third pixel in thepre-corrected image may be determined. The third pixel may correspond toa first pixel in the original image and a second pixel in the errorimage. The weighting coefficient of the second pixel in the error imagemay be determined based on the information entropy of the third pixel inthe pre-corrected image.

In some embodiments, the weighting coefficient of the second pixel inthe error image may be determined when the information entropy of thethird pixel in the pre-corrected image is minimum.

In some embodiments, a first neighborhood matrix of the first pixel inthe original image and a second neighborhood matrix of the second pixelin the error image may be determined. A third neighborhood matrix of thethird pixel in the pre-corrected image may be determined based on thefirst neighborhood matrix and the second neighborhood matrix. Theinformation entropy of the third pixel in the pre-corrected image may becalculated based on the third neighborhood matrix.

In some embodiments, the size of the first neighborhood matrix or thesize of the second neighborhood matrix may be determined based on afeature of the error image.

In some embodiments, the size of the first neighborhood matrix or thesize of the second neighborhood matrix may be between 9 and 31 pixels.

In some embodiments, the original image, the pre-corrected image, theerror image, or the corrected image may be compressed according to apreset field of view.

In some embodiments, a compensation may be performed on the correctedimage.

In some embodiments, a high frequency part of the original image may besegmented. A low frequency part of the corrected image may be segmented.The high frequency part of the original image and the low frequency ofthe corrected image may be fused.

In a second aspect of the present disclosure, a method for imagecorrection is provided. The method may include one or more of thefollowing operations. An original image may be received. An imagerelating to a region of interest may be obtained. An artifact in theimage relating to the region of interest may be detected. An artifactimage may be generated based on the artifact. The original image may becorrected based on the artifact image.

In some embodiments, the original image may be segmented into aplurality of portions, the plurality of portions include an air portionand a tissue portion. Candidate regions of interest of the originalimage may be determined. The region of interest may be determined fromthe candidate regions of interest based on the plurality of portions,wherein in the region of interest, a percentage of the tissue portion isbelow a third threshold.

In some embodiments, an angle range may be determined. A processingangle may be selected from the angle range. The image relating to theregion of interest may be rotated by the processing angle. The rotatedimage may be smoothed to obtain a smoothed image. A subtracted image maybe generated based on the original image and the smoothed image. Theartifact may be detected in the subtracted image.

In some embodiments, whether the detected artifact is a potential tissuemay be determined to obtain a determination result. A compensation maybe performed based on the determination result to the detected artifact.

In a third aspect of the present disclosure, a system for imagecorrection is provided. The system may include a pre-correction block, ageneration block, and a weighting block. The pre-correction block may beconfigured to receive an original image; and pre-correct the originalimage. The generation block may be configured to generate correctiondata based on the original image and the pre-corrected image. Theweighting block may be configured to weight the original image and thepre-corrected image based on the correction data; and generate acorrected image based on the weighting.

In a fourth aspect of the present disclosure, a system for imagecorrection is provided. The system may include a determination block, adetection block, an image creation block, and a processing block. Thedetermination block may be configured to receive an original image; andobtain an image relating to a region of interest. The detection blockmay be configured to detect an artifact in the image relating to theregion of interest. The image creation block may be configured togenerate an artifact image based on the artifact. The processing blockmay be configured to correct the original image based on the artifactimage.

In a fifth aspect of the present disclosure, a device including memorystoring instructions and at least one processor that executes theinstructions to perform operations. When the at least one processorexecuting the instructions, the at least one processor may perform oneor more of the following operations. An original image may be received.The original image may be pre-corrected. Correction data may begenerated based on the original image and the pre-corrected image. Theoriginal image and the pre-corrected image may be weighted based on thecorrection data. A corrected image may be generated based on theweighting.

In a sixth aspect of the present disclosure, a device including memorystoring instructions and at least one processor that executes theinstructions to perform operations. When the at least one processorexecuting the instructions, the at least one processor may perform oneor more of the following operations. An original image may be received.An image relating to a region of interest may be obtained. An artifactin the image relating to the region of interest may be detected. Anartifact image may be generated based on the artifact. The originalimage may be corrected based on the artifact image.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a block diagram depicting an imaging system according to someembodiments of the present disclosure;

FIG. 2 is a flowchart of signal processing according to some embodimentsof the present disclosure;

FIG. 3 is a block diagram illustrating an architecture of a processingmodule according to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating a process for correcting an imageaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an architecture of a correctionunit according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure;

FIG. 7 is an exemplary flowchart illustrating a process forpre-correcting an image according to some embodiments of the presentdisclosure;

FIG. 8 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure;

FIG. 9 is a flowchart illustrating a process for determining theweighting coefficients of the projection data of the pre-corrected imageaccording to some embodiments of the present disclosure;

FIG. 10 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure;

FIG. 11 is a flowchart illustrating a process for determining theweighting coefficient of a pixel in the error image according to someembodiments of the present disclosure;

FIG. 12 is a flowchart illustrating a process for image compensationaccording to some embodiments of the present disclosure;

FIG. 13-A is a block diagram illustrating an architecture of acorrection unit according to some embodiments of the present disclosure;

FIG. 13-B is a flowchart illustrating a process for correcting an imageaccording to some embodiments of the present disclosure;

FIG. 14 is a flowchart illustrating a process for determining an imagerelating to a region of interest (ROI) according to some embodiments ofthe present disclosure;

FIG. 15 is a flowchart illustrating a process for generating an artifactimage according to some embodiments of the present disclosure; and

FIG. 16-A through FIG. 16-C illustrate an exemplary process forgenerating a corrected image according to some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirits andscope of the present disclosure. Thus, the present disclosure is notlimited to the embodiments shown, but to be accorded the widest scopeconsistent with the claims.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theymay achieve the same purpose.

It will be understood that when a unit, module or block is referred toas being “on,” “connected to” or “coupled to” another unit, module, orblock, it may be directly on, connected or coupled to the other unit,module, or block, or intervening unit, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

FIG. 1 is a block diagram of an imaging system 100 according to someembodiments of the present disclosure. It should be noted that theimaging system 100 described below is merely provided for illustrationpurposes, and not intended to limit the scope of the present disclosure.The radiation used herein may include a particle ray, a photon ray, orthe like, or any combination thereof. The particle ray may includeneutron, proton, electron, p-meson, heavy ion, or the like, or anycombination thereof. The photon beam may include X-ray, y-ray,ultraviolet, laser, or the like, or any combination thereof. The imagingsystem may find its applications in different fields such as, forexample, medicine or industry. Merely by way of example, the imagingsystem may be a positron emission tomography (PET) system, a singlephoton emission computed tomography (SPECT) system, a computedtomography (CT) system, a digital radiography (DR) system, amulti-modality system, or the like, or any combination thereof.Exemplary multi-modality system may include a computedtomography-positron emission tomography (CT-PET) system, a positronemission tomography-magnetic resonance imaging (PET-MRI) system, acomputed tomography-positron emission tomography-magnetic resonanceimaging (CT-PET-MRI) system, etc. As another example, the system may beused in internal inspection of components including, e.g., flawdetection, security scanning, failure analysis, metrology, assemblyanalysis, void analysis, wall thickness analysis, or the like, or anycombination thereof.

As illustrated in FIG. 1, the imaging system 100 may include anacquisition module 110, a control module 120, a storage module 130, aprocessing module 140, and a display 150.

The acquisition module 110 may detect radiation rays, or receiveinformation regarding detected radiation rays in the imaging system. Theacquisition module 110 may include or communicate with a scanner (e.g.,a PET scanner, a CT scanner, or the like, or a combination thereof).Merely by way of example, the radiation rays may take the form of lineof response (LOR) in a PET system. Detection of the LORs may beperformed by the acquisition module 110 by way of counting values ofcoincidence from annihilation of positrons. As another example, theradiation rays may be X-ray beams passing through an object (e.g., apatient) in a CT system. The intensity of an X-ray beam passing throughthe object that lies between the X-ray source and a detector (not shown)may be attenuated, and further evaluated by the acquisition module 110.In some embodiments, the ROM may store programs for imaging of varioustypes of nuclear medicine diagnosis. Exemplary types of nuclear medicinediagnosis may include PET, SPECT, CT, MRI, or the like, or a combinationthereof. It should also be noted here that the “line of response” or“LOR” used here may be representative of a radiation ray, and notintended to limit the scope of the present disclosure. The radiation rayused herein may include a particle ray, a photon ray, or the like, orany combination thereof. The particle ray may include neutron, proton,electron, p-meson, heavy ion, or the like, or any combination thereof.For example, the radiation ray may represent the intensity of an X-raybeam passing through the subject in the case of a CT system. As anotherexample, the radiation ray may represent the probability of a positrongenerated in the case of a PET system.

The acquisition module 110 may select data to be further processed fromthe original data. The acquisition module 110 may measure the number ofradiation rays incident on the detector and determine, for example, theline of response (LOR) in the case of PET, the projected X-rays thatpass through a subject in the case of CT, etc. In some embodiments, theacquisition module 110 may be a coincidence counting circuit in a PETcase. For instance, from a subject (e.g., a patient, etc.) who has takena radioactive drug (e.g., 11C, 13N, 150, or the like), two gamma raysmay be generated by the annihilation of a positron. The gamma rays maybe detected or registered by two opposing detector units of the PETsystem. For example, a coincidence counting circuit may check theincidence of the gamma rays, and determine the registered event to beproper data when the gamma rays impinge on the detector (not shown) atthe opposite sides of the patient at or around the same time. Thecoincidence counting circuit may be part of the acquisition module 110.In some embodiments, the acquisition module 110 may be designed tosurround a subject to form a table type scanner 160 (e.g., a CTscanner).

The control module 120 may control the acquisition module 110, thestorage module 130, the processing module 140, and the display 150. Thecontrol module 120 may receive information from and send information tothe acquisition module 110, the storage module 130, the processingmodule 140, and/or the display 150. In some embodiments, the controlmodule 120 may control the operation of the acquisition module 110.Merely for example, the control module 120 may control whether toacquire a signal, or the time when the next signal acquisition mayoccur. As another example, the control module 120 may control whichsection of radiation rays may be processed during an iteration of thereconstruction. The control module 120 may control the processing module140, for example, to select different algorithms to process the raw dataof an image, to determine the iteration times of the iterationprojection process, and/or the location of the radiation rays. In someembodiments, the control module 120 may receive a real-time or apredetermined command from the display 150 provided by a user including,e.g., an imaging technician, or a doctor, and adjust the acquisitionmodule 110, and/or the processing module 140 to take images of a subjectof interest according to the received command. In some embodiments, thecontrol module 120 may communicate with the other modules for exchanginginformation relating to the operation of the scanner or other parts ofthe imaging system 100.

The storage module 130 may store the acquired signals, the controlparameters, the processed signals, or the like. In some embodiments, thestorage module 130 may include a random access memory (RAM), a read onlymemory (ROM), for example, a hard disk, a floppy disk, a cloud storage,a magnetic tape, a compact disk, a removable storage, or the like, or acombination thereof. The removable storage may read from and/or writedata to a removable storage unit in a certain manner. The storage devicemay also be other similar means for loading computer programs or otherinstructions into the computer or processor.

The processing module 140 may process different kinds of informationreceived from different units. In some embodiments, the processingmodule 140 may process the signals acquired by the acquisition module110, or stored in the storage module 130. In some embodiments, theprocessing module 140 may generate images, reports including one or moreimages and/or other related information, or the like, or a combinationthereof. In some embodiments, the processing module 140 may process theinformation displayed in the display 150.

The display 150 may receive input and/or display output information. Thedisplay may include a liquid crystal display (LCD), a light emittingdiode (LED)-based display, or any other flat panel display, or may use acathode ray tube (CRT), a touch screen, or the like. A touch screen mayinclude, e.g., a resistance touch screen, a capacity touch screen, aplasma touch screen, a vector pressure sensing touch screen, an infraredtouch screen, or the like, or a combination thereof.

Further, while not shown, the imaging system 100 may be connected to anetwork (e.g., a telecommunications network, a local area network (LAN),a wireless network, a wide area network (WAN) such as the Internet, apeer-to-peer network, a cable network, etc.) for communication purposes.

For further understanding the present disclosure, several examples aregiven below, but the examples do not limit the scope of the presentdisclosure. For example, in some embodiments, the processing module 140may process signals received from the acquisition module 110 andgenerate one or more images based on these signals and deliver theimages to the display 150. In some embodiments, the processing module140 may process data input by a user or an operator via the display 150and transform the data into specific commands, and supply the commandsto the control module 120. The display 150 may receive input and/ordisplay output information. The input and/or output information mayinclude programs, software, algorithms, data, text, number, images,voice, or the like, or any combination thereof. For example, a user oran operator may input some initial parameters or conditions to initiatea scan. As another example, some information may be imported from anexternal resource, such as a floppy disk, a hard disk, a wirelessterminal, or the like, or any combination thereof. In some embodiments,the control module 120, the storage module 130, the processing module140, and/or the display 150 may be integrated into a console 170. Viathe console 170, a user may set parameters for scanning, control theimaging procedure, view the images produced through the console 170, orthe like, or a combination thereof.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the imaging system 100 may be varied or changedaccording to specific implementation scenarios. Merely by way ofexample, some other components may be added into the imaging system 100,such as a patient positioning module, a gradient amplifier module, andother devices or modules. As another example, the storage module 130 isunnecessary and the modules or modules in the imaging system 100 mayinclude an integrated storage unit respectively. Note that the imagingsystem may be a traditional or a single-modality medical system, or amulti-modality system including, e.g., a positron emissiontomography-magnetic resonance imaging (PET-MRI) system, a single photonemission computed tomography-magnetic resonance imaging (SPECT-MRI)system, a remote medical MRI system, and others, etc. However, thosevariations and modifications do not depart from the scope of the presentdisclosure.

FIG. 2 is a flowchart of signal processing according to some embodimentsof the present disclosure. In step 210, a parameter may be set. Theparameter may be set by the control module 120. In some embodiments, theparameter may include a parameter relating to an acquisition process, aparameter relating to a storing process, a processing parameter, aparameter relating to a displaying process, or the like, or acombination thereof. Merely by way of example, the parameter may includecurrent, voltage, a scanning protocol designed for one or more tissuesto be imaged, diseases, and/or clinical scenarios, sampling speed,sampling frequency, storage speed, storage volume management, imagereconstruction method, or the like, or a combination thereof. In someembodiments, the parameter may be set via the console 170.

In step 220, a signal may be acquired. The signal may be a PET signal, aCT signal, a SPECT signal, an MRI signal, or the like, or a combinationthereof. In some embodiments, the signal acquisition may be performed bythe acquisition module 110. In some embodiments, the signal may beacquired from the storage module 130. In some embodiments, the signalmay be retrieved from an external device or via a user input. In step230, the acquired signal may be stored. The acquired signal may bestored in the storage module 130 or any storage disclosed anywhere inthe present disclosure. In some embodiments, step 220 and step 230 maybe integrated into a single step in which the signal may be acquired andstored simultaneously or successively.

In step 240, the signal may be processed. The processing may beperformed by the processing module 140. During the processing, one ormore processing parameters may be set. In some embodiments, the signalmay be processed to reconstruct an image (e.g., a PET image, a CT image,a SPECT image, an MRI image, or the like). In some embodiments, thereconstructed image may be further corrected to remove or reduceartifact(s). In some embodiments, the reconstructed image may be furtherprocessed and a report including the reconstructed image may begenerated. In some embodiments, the reconstructed image and/or thegenerated report may be transmitted to a related device (e.g., aterminal, a database, or the like). In some embodiments, thereconstructed image and/or the generated report may be transmitted to arelated device to be further processed (e.g., to be printed, to bedisplayed, or the like).

It should be noted that the above description is provided for thepurposes of illustration, not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be reduced to practice in the light ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, step230 may be unnecessary, and the acquired signal may be processeddirectly in step 240 without storing. As another example, the parametermay be set during any step of the entire process.

FIG. 3 is a block diagram illustrating an architecture of a processingmodule according to some embodiments of the present disclosure. In someembodiments, the processing module 140 may include a processing unit310, a reconstruction unit 320, a correction unit 330, and a storageunit 340. In some embodiments, at least two of the units may beconnected with each other via a wired connection (e.g., a metal cable,an optical cable, a hybrid cable, or the like, or any combinationthereof) or a wireless connection (e.g., a Local Area Network (LAN), aWide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or any combination thereof). In someembodiments, the units may be connected with each other through amedium. The medium may include a visible medium or an invisible medium(e.g., radio, optical, sonic, electromagnetic induction, etc.).

The processing unit 310 may process different kinds of informationreceived from the control module 120, the storage module 130, and/or thedisplay module 150. The information may include a signal (e.g., a CTsignal), information regarding a subject, a control parameter (e.g.,acquisition frequency, acquisition speed, or the like), a displayparameter (e.g., brightness, resolution ratio, scale, or the like), orthe like, or a combination thereof. Merely by way of example, theprocessing unit 310 may process the signal, e.g., remove or reducenoises, filter the signal, convert the signal from an analog signal to adigital signal, or the like, or a combination thereof.

The reconstruction unit 320 may generate an image relating to an object(e.g., a portion of a subject). The reconstruction unit 320 may employdifferent kinds of image reconstruction techniques for the imagereconstruction procedure. Exemplary image reconstruction techniques mayinclude Fourier slice theorem, filtered back projection algorithm,fan-beam reconstruction, iterative reconstruction, or the like, or acombination thereof. In some embodiments, the reconstruction unit 320may include one or more sub-units (not shown). The sub-units mayreconstruct images by employing different reconstruction methods. Insome embodiments, the reconstructed image may be stored in the storageunit 340.

The correction unit 330 may correct the reconstructed image. In someembodiments, the image may be derived from the reconstruction unit 320or the storage unit 340. Merely by way of example, the correction unit330 may remove or reduce artifacts in the reconstructed image. In someembodiments, the artifact may include a streak artifact, a ringartifact, a motion artifact, or the like, or any combination thereof. Insome embodiments, the correction method may include an iterative method,an interpolation method, or the like, or any combination thereof.

The storage unit 340 may store the information processed by theprocessing unit 310, the image reconstructed by the reconstruction unit320, and the image corrected by the correction unit 330. In someembodiments, the storage format may include text, picture, audio, video,code, or the like, or a combination thereof. In some embodiments, one ormore algorithms that may be used during the processing, thereconstruction, or the correction may be stored in the storage unit 340.The algorithm may include a threshold segmentation method, an iterativemethod, an interpolation method, a statistical algorithm, a smoothingfiltering method, or the like, or any combination thereof.

It should be noted that the above description of the processing moduleis merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. However, those variationsand modifications do not depart from the scope of the presentdisclosure. For example, the assembly and/or function of the processingmodule may be varied or changed. In some embodiments, one or more unitsin the processing module 140 may include an independent storage block(not shown) respectively. In some embodiments, any two or more units maybe integrated into an independent unit used to implement more than onefunctions. However, those variations and modifications do not departfrom the scope of the present disclosure.

FIG. 4 is a flowchart illustrating a process for correcting an imageaccording to some embodiments of the present disclosure. In step 402, asignal may be received. The signal may be acquired by the acquisitionmodule 110, or retrieved from the storage module 130 or an externalresource including, for example, a floppy disk, a hard disk, a wiredterminal, a wireless terminal, or the like, or any combination thereof.In some embodiments, the signal may be a CT signal.

In step 404, the signal may be processed. The processing may beperformed by the processing unit 310. Merely by way of example, one ormore interference factors (e.g., noises) may be removed or reduced. Asanother example, format of the signal may be changed, for example, thesignal may be converted from an analog signal to a digital signal. As afurther example, the signal may be amplified, filtered, or the like, ora combination thereof. In some embodiments, during the processing, oneor more processing parameters may be set. The processing parameter(s)may include or relate to methods used to improve or adjust noises,processing speed, processing frequency, or the like, or a combinationthereof.

In step 406, an image may be reconstructed based on the signal. Thereconstruction may be performed by the reconstruction unit 320. Theimage may include a CT image, a SPECT image, or the like, or acombination thereof. In some embodiments, the image may relate to aportion of the subject (e.g., a head, a breast, a lung, a pleura, amediastinum, an abdomen, a long intestine, a small intestine, a bladder,a gallbladder, a triple warmer, a pelvic cavity, a backbone, a bloodvessel, or the like, or any combination thereof). In some embodiments,one or more reconstruction algorithms may be applied. Merely by way ofexample, an iterative process of forward projection and backwardprojection may be performed.

In step 408, the image may be corrected. The image may be corrected bythe correction unit 330. In some embodiments, the correction process maybe performed in an image domain or in a projection domain. In someembodiments, the correction process may be performed in a domain thatmay be switched at a time. Merely by way of example, a first step of thecorrection process may be performed in the image domain, and a secondstep of the correction process may be performed in the projectiondomain.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. For example, in some embodiments, step 404 maybe unnecessary. As another example, step 404 and step 406 may be mergedinto an independent step in which the signal may be processed and theimage may be reconstructed simultaneously or successively. As a furtherexample, one or more other optional steps may be added into theexemplary process illustrated in FIG. 4. Examples of such steps mayinclude storing or caching a received or processed signal, areconstructed or corrected image, or the like. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 5 is a block diagram illustrating an architecture of a correctionunit according to some embodiments of the present disclosure. As shownin FIG. 5, the correction unit 330 may include a pre-correction block510, a generation block 520, a weighting block 530, and a compensationblock 540. In some embodiments, at least two of the blocks may beconnected with each other via a wired connection (e.g., a metal cable,an optical cable, a hybrid cable, or the like, or any combinationthereof) or a wireless connection (e.g., a Local Area Network (LAN), aWide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or any combination thereof). In someembodiments, the blocks may be connected with each other through amedium. The medium may include a visible medium or an invisible medium(e.g., radio, optical, sonic, electromagnetic induction, etc.).

The pre-correction block 510 may pre-correct an original image. Theoriginal image may be reconstructed by the reconstruction unit 320, orretrieved from the storage unit 340, an external resource (e.g., a harddisk, a floppy disk, a wireless terminal, or the like, or a combinationthereof), or any storage disclosed anywhere in the present disclosure.In some embodiments, the original image may be a CT image that may bereconstructed by the reconstruction unit 320. In some embodiments, thepre-correction block 510 may pre-correct the original image by apre-correction method. The pre-correction method may include aniterative reconstruction method, a projection interpolation method, orthe like, or a combination thereof. In some embodiments, a pre-correctedimage may be generated and transmitted to the generation block 520,and/or stored in a storage block (not shown).

The generation block 520 may generate correction data. As used herein,the correction data may include projection data of the original image,projection data of the pre-corrected image, an error image, or the like,or a combination thereof. As used herein, projection data may refer totwo-dimensional data of an image that is projected onto a specificprojection plane. As used herein, an error image may refer to an imagethat may include the difference between the original image and thepre-corrected image. For example, the error image may be generated bysubtracting the pre-corrected image from the original image. In someembodiments, the correction data may further include informationregarding the subject (e.g., name, age, gender, a medical history, orthe like, or a combination thereof), a scanning parameter (e.g.,intensity of the X-ray, current, voltage, or the like, or a combinationthereof), a reconstruction parameter (e.g., a reconstruction method,etc.), or the like, or a combination thereof.

The weighting block 530 may perform a weighting operation on theoriginal image and/or the pre-corrected image based on the correctiondata and generate a corrected image (also referred to as a “correctionprocess”). As used herein, “weighting” may refer to assigning aweighting factor to a parameter including, for example, a greyscalevalue of a pixel, projection data of the pixel, etc. For instance,performing a weighting operation with respect to an image may includingassigning weighting factors to the greyscale values of various pixels inthe image. As another example, performing a weighting operation withrespect to two images including a first image and a second image mayinclude assigning weighting factors to greyscale values of pixels in thefirst image and greyscale values of corresponding pixels in the secondimage. As used herein, a corresponding pixel of a pixel in the firstimage may refer to a pixel in the second image whose coordinates are thesame with that of the pixel in the first image. In some embodiments, theweighting operation may be performed in a projection domain or in animage domain. For example, the original image and the pre-correctedimage may be weighted in projection domain based on projection data ofthe original image and that of the pre-corrected image. As anotherexample, the original image and the error image may be weighted in animage domain. In some embodiments, the weighting block 530 may determineone or more weighting coefficients for the weighting operation. Forexample, a plurality of weighting coefficients corresponding to pixelsin the error image may be determined.

The compensation block 540 may perform a compensation on the correctedimage. As used herein, a compensation may refer to that usefulinformation lost during the correction process may be compensated in thecorrected image. For example, a high frequency part may be extractedfrom the original image and compensated in the corrected image.

It should be noted that the above description is provided for thepurposes of illustration, not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be reduced to practice in the light ofthe present disclosure. For example, any two or more blocks in thecorrection unit 330 may be integrated into an independent block. Asanother example, the compensation block 540 may be unnecessary. As afurther example, the correction unit 330 may include a storage block(not shown), or the blocks in the correction unit 330 may include anindependent storage sub-block respectively. However, those variationsand modifications do not depart from the scope of the presentdisclosure.

FIG. 6 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure. The process forimage correction may be performed by the correction unit 330. In step602, an original image may be acquired. The original image may beacquired from the reconstruction unit 320, the storage unit 340, anexternal resource (e.g., a hard disk), or any storage disclosed anywherein the present disclosure. Merely by way of example, the original imagemay be a CT image. The CT image may be reconstructed based on a CTsignal acquired by a CT scanning performed on a subject. The CT scanningmay be performed by the acquisition module 110 or a CT scanner that maycommunicate with the acquisition module 110. In some embodiments, the CTscanning may be performed on a portion (e.g., a head, a lung, anabdomen, a breast, a pleura, or the like) of the subject. In someembodiments, the original image may be presented in a field of view(FOV). The FOV may include a default FOV, a full FOV, or an adjustedFOV. For example, if in the default FOV, a region including artifact(s)to be corrected is hidden, a full FOV may be needed. In someembodiments, the format of the original image may include Bitmap (BMP),Joint Photo Graphic Experts Group (JPEG), Tagged Image File Format(TIFF), Graphics Interchange Format (GIF), Portable Network Graphic(PNG), Exchangeable Image File (EXIF), or the like, or a combinationthereof.

In step 604, the original image may be pre-corrected by a pre-correctionmethod. The pre-correction may be performed by the pre-correction block510. The pre-correction may be performed in a projection domain or in animage domain. The pre-correction method may include an iterativereconstruction method, a projection interpolation method, or the like,or a combination thereof. For example, by the projection interpolationmethod, region(s) with artifact(s) may be removed and replaced by aninterpolation of projection data in a nearby region. As another example,in the iterative reconstruction method, raw projection data that do notinclude artifact(s) (e.g., a metal artifact) may be used. An initialimage may be retrieved and projection data of the initial image may becompared with the raw projection data. Then the initial image may beiteratively updated in order to approach the raw projection data.Finally, a pre-corrected image may be obtained. In some embodiments, theinitial image may be an empty image or the original image to becorrected. Examples of iterative reconstruction methods may includealgebraic reconstruction technique (ART), simultaneous algebraicreconstruction technique (SART), iterated filter back-projection (IFBP),statistical iterative image reconstruction techniques, or the like, or acombination thereof. Description regarding the iterative reconstructionmethod may be found in, for example, Wang et al., “Iterative deblurringfor CT metal artifact reduction,” IEEE Transactions on Medical Imaging,15(5): 657-664 (1996), which is hereby incorporated by reference.

In step 606, correction data may be generated based on the originalimage and the pre-corrected image. The correction data may be generatedby the generation block 520. In some embodiments, the correction datamay include projection data of the original image, projection data ofthe pre-corrected image, an error image, projection data of the errorimage, or the like, or a combination thereof. As used herein, an errorimage may refer to an image that may include the difference between theoriginal image and the pre-corrected image. For example, the error imagemay be generated by subtracting the pre-corrected image from theoriginal image.

In step 608, the original image and the pre-corrected image may beweighted based on the correction data. The weighting operation may beperformed by the weighting block 530. The weighting operation may beperformed in a projection domain or in an image domain. In someembodiments, the projection data of the original image and/or theprojection data of the pre-corrected image may be weighted in theprojection domain. In some embodiments, the original image and/or theerror image may be weighted in an image domain. In some embodiments, theoriginal image and/or the error image may be weighted in a projectiondomain. In some embodiments, one or more weighting coefficients may bedetermined for the weighting operation.

In step 610, a corrected image may be generated. The corrected image maybe generated by the weighting block 530. In some embodiments, thecorrected image may be generated by transforming the weighted projectiondata of the original image and/or that of the pre-corrected image to animage domain. In some embodiments, the corrected image may be directlygenerated by weighting the error image and the original image.

In step 612, compensation may be performed on the corrected image. Thecompensation may be performed by the compensation block 540. In someembodiments, the compensation may be performed by fusing a highfrequency part of the original image, and/or a high frequency part ofthe corrected image, and/or a low frequency part of the corrected image.For example, the high frequency part of the original image may besegmented and combined into the corrected image. In some embodiments, aweighing mask may be used on the original image. The weighting mask mayinclude weighting coefficients for various pixels in the original image.For example, for a pixel close to a metal artifact, a relatively largeweighting coefficient (e.g., close to 1) may be chosen; for a pixeldistant to a metal artifact, a relatively small weighting coefficient(e.g., transitioning from 1 to 0) may be chosen.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. For example, one or more other optional stepsmay be added between any two steps in the exemplary process illustratedin FIG. 6. Examples of such steps may include storing or caching areceived or processed signal, a reconstructed or corrected image, or thelike. As another example, step 608 and step 610 may be merged into anindependent step in which the weighting operation and the generation ofthe corrected image may be performed simultaneously or successively.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 7 is an exemplary flowchart illustrating a process forpre-correcting an image according to some embodiments of the presentdisclosure. The pre-correction process may be performed by thepre-correction block 510. For illustration purposes, a pre-correctionfor an original image including metal artifact(s) may be describedbelow.

In step 720, an original image may be acquired. The original image maybe acquired from the reconstruction unit 420, the storage unit 340, orany storage disclosed anywhere in the present disclosure. In step 722, ametal image may be determined. As used herein, a metal image may referto an image including only metal information of the original image. Ingeneral, the X-ray absorption property of metal (e.g., a metal object)may be higher than that of a tissue or bone of the subject, andtherefore the attenuation coefficient of metal may be higher than thatof a tissue or bone. A streak artifact or a starburst artifact may begenerated in the original image due to the difference of the attenuationcoefficient of metal and the attenuation coefficient of a tissue or boneof the subject. In the original image, the CT value (also referred to as“CT HU (Hounsfield unit)) of the metal artifact may be higher than thatof the tissue or bone of the subject.

In some embodiments, the metal image may be determined by a segmentationmethod. Exemplary segmentation methods may include a thresholdingmethod, a clustering method, a compression-based method, ahistogram-based method, an edge detection method, or the like, or thelike, or any combination thereof. As an example, the metal image may bedetermined according to Formula (1):

$\begin{matrix}{I_{metal} = \left\{ {\begin{matrix}{Iori} & {{{if}\mspace{14mu}{Iori}} > T_{metal}} \\0 & {else}\end{matrix},} \right.} & (1)\end{matrix}$

where I_(metal) may represent a set of CT values of pixels in the metalimage, Iori may represent a set of CT values of the pixels in theoriginal image, Tmetal may represent a segmentation threshold. If the CTvalue of a pixel in the original image exceeds the segmentationthreshold T_(metal), the pixel may be determined as a metal pixel in themetal image. Besides the metal pixels, CT values of other pixels in themetal image may be set as 0.

In some embodiments, the segmentation threshold may be adjustedaccording to an adjusting parameter. The adjusting parameter may includeor relate to the portion that is scanned (e.g., the head, the neck, orthe like), the scanning voltage, the scanning current, the convolutionkernel used during the reconstruction, or the like, or a combinationthereof. For example, for an original image acquired from body of asubject by a scanning voltage of 120 keV and reconstructed by a smoothconvolution kernel, the segmentation threshold may be selected from arange of 2500-2800 Hounsfield unit (HU).

In step 724, the projection data of the original image and theprojection data of the metal image may be computed. In some embodiments,a projection operation may be performed on the original image and/or themetal image. In some embodiments, during the reconstruction of theoriginal image, the acquired signal may be transformed to the projectiondomain and corresponding projection data may be generated. The generatedprojection data may be stored in any storage disclosed anywhere in thepresent disclosure. Therefore, in step 724 the projection data of theoriginal image may be retrieved from the storage.

In step 726, the projection region of the artifact(s) in the projectiondomain may be determined based on the projection data of the originalimage and that of the metal image. In some embodiments, the projectionregion of the artifact(s) may be determined by comparing the differencebetween the projection data of the original image and that of the metalimage.

In step 728, an interpolation may be performed based on the projectionregion of the artifact(s) in the projection domain. In some embodiments,the interpolation may be performed in the projection data of theoriginal image, or in the difference between the projection data of theoriginal image and that of the metal image. In some embodiments, theinterpolation method may include linear interpolation, polynomialinterpolation, spline interpolation, or the like, or a combinationthereof.

As an example, a linear interpolation may be performed on the differencebetween the projection data of the original image and that of the metalimage. The linear interpolation may be described in Formula (2):

$\begin{matrix}{{{{Pcorr}\; 1_{p,k}} = {{{Pdiff}_{p,S}\frac{E - K}{E - S}} + {{Pdiff}_{p,E}\frac{K - S}{E - S}}}},} & (2)\end{matrix}$

where P may represent an projection angle, S may represent a startchannel of the projection region of the artifact(s), E may represent anend channel of the projection region of the artifact(s), and K mayrepresent a specific channel of the projection region of theartifact(s). Pcorr1_(p,k) may represent the projection data of thechannel K at the projection angle P after the interpolation, Pdiff_(p,S)may represent projection data of the start channel S at the projectionangle P, and Pdiff_(p,E) may represent projection data of the endchannel E at the projection angle P.

After the interpolation, pre-corrected projection data may be generated.In some embodiments, the pre-corrected projection data may be stored inany storage disclosed anywhere in the present disclosure and may beloaded if needed. In some embodiments, a pre-corrected image may begenerated based on the pre-corrected projection data in step 730. Thepro-corrected image may be stored in any storage disclosed anywhere inthe present disclosure and may be loaded if needed.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. For example, the pre-corrected method mayfurther include iterative reconstruction method (e.g., see FIG. 6) or acombination of the iterative reconstruction method and the interpolationmethod. As another example, the interpolation method may further includepolynomial interpolation, spline interpolation, or the like, or acombination thereof. However, those variations and modifications do notdepart from the scope of the present disclosure.

FIG. 8 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure. In step 802,projection data of the original image may be determined. As described inFIG. 7, the projection data of the original image may be retrieved fromany storage disclosed anywhere in the present disclosure, or generatedby transforming the original image from the image domain to theprojection domain. In some embodiments, the projection data of theoriginal image may be referred to as the “first projection data.”

In step 804, projection data of the pre-corrected image may bedetermined. As described in FIG. 7, during the pre-correction, theprojection data of the pre-corrected image may be generated. Therefore,in some embodiments, the projection data of the pre-corrected image alsomay be retrieved from the results obtained in a previous step. In someembodiments, the projection data of the pre-corrected image may bedetermined by transforming the pre-corrected image from the image domainto the projection domain. In some embodiments, the projection data ofthe pre-corrected image may be referred to as the “second projectiondata.”

In step 806, weighting coefficients (e.g., W_(p,k) in Formula (4)) ofthe projection data of the pre-corrected image may be determined. Asdescribed in FIG. 7, in the pre-corrected image, the pixelscorresponding to the artifact(s) may be pre-corrected or replaced.However, during the pre-correction, some original useful information(e.g., data regarding tissues or bones) may be lost. Therefore, aweighting operation may be needed to be performed on the original imageand the pre-corrected image, in order to compensate the lost usefulinformation. In some embodiments, the weighting coefficients may bepre-selected empirical values, values selected according to a defaultsetting of the system 100, values determined based on features (e.g.,shape, size, etc.) of specific artifact(s) (see, for example, FIG. 9 andthe description thereof), etc.

Merely by way of example, for an original image including metalartifact(s), the weighting coefficients of the projection data of thepre-corrected image may be adjusted according to a weighting intensityas shown in Formula (3) below:

$\begin{matrix}{{W_{p,k} = {1 + {W_{0}*\left( {{{0.5}*{\sin\ \left( {{\pi\frac{1 - {\max\left( {{Pm{{etal}_{p,k}/{\max\left( {Pmetal} \right)}}},\left( {1 - W_{0}} \right)} \right)}}{W_{0}}},\ {k + {{0.5}*\pi}}} \right)}} - {0{.5}}} \right)}}},} & (3)\end{matrix}$

where W₀ may represent the weighting intensity, Pmetal_(p,k) mayrepresent the projection data of the metal image corresponding tochannel K and projection angle P, max(Pmetal) may represent the maximumvalue of the projection data of the metal image. In some embodiments, W₀may be an empirical value (e.g., 1). In some embodiments, the value ofW₀ may be determined based on the shape of the metal (see, for example,FIG. 9 and the description thereof).

In step 808, a corrected image may be generated based on the projectiondata of the original image, the projection data of the pre-correctedimage, and the weighting coefficients determined in step 806. In someembodiments, a weighting operation may be performed according to Formula(4):

$\begin{matrix}{{{Pcorr_{p,k}} = {{W_{p,k}Pcorr1_{p,k}} + {\left( {1 - W_{p,k}} \right)Pori_{p,K}}}},} & (4)\end{matrix}$

where Pcorr_(p,k) may represent a weighted result of the projection dataof the original image and the projection data of the pre-correctedimage. In some embodiments, the weighted result Pcorr_(p,k) may bereferred to as the “third projection data”. Pori_(p,k) may represent theprojection data of the original image (also referred to as the “firstprojection data”) corresponding to channel K and projection angle P,Pcorr1_(p,k) may represent the projection data of the pre-correctedimage (also referred to as the “second projection data”) correspondingto channel K and projection angle P, and W_(P,K) may represent theweighting coefficient of the projection data of the pre-corrected imagecorresponding to channel K and projection angle P.

In some embodiments, the corrected image may be generated by performinga backward projection (i.e., transforming from the projection domain tothe image domain) on the third projection data.

In some embodiments, before the corrected image is generated, the thirdprojection data may be further corrected. For example, a smoothingoperation and/or an interpretation may be performed on the firstprojection data (also referred to as “the projection data of theoriginal image”) based on the third projection data.

Merely by way of example, difference between the first projection dataand the third projection data may be determined. A backward projection(i.e., transforming from the projection domain to the image domain) maybe performed on the difference, and a first intermediate metal image maybe generated. An intermediately corrected image may be generated bysubtracting the first intermediate metal image and the metal image fromthe original image.

In some embodiments, a segmentation operation may be performed on theintermediately corrected image to obtain a model image. In someembodiments, the segmentation operation may be performed by athresholding method as shown in Formula (5) below:

$\begin{matrix}{I_{model} = \left\{ {\begin{matrix}{0,} & {I \leq T_{{tiss}ue}} \\{{1000},} & {T_{{tiss}ue} \leq I \leq T_{bone}} \\{1,} & {I \geq T_{bone}}\end{matrix},} \right.} & (5)\end{matrix}$

where I_(model) may represent a set of CT values of pixels in the modelimage, T_(tissue) may represent a tissue threshold, and T_(bone) mayrepresent a bone threshold. In some embodiments, the tissue thresholdT_(bone) and the bone threshold T_(bone) may be determined based on adefault setting of the system 100, or set by an operator (e.g., adoctor, an imaging technician, etc.).

In some embodiments, projection data of the model image may begenerated. A smoothing operation may be performed on the firstprojection data based on the projection data of the model image. As usedherein, a smoothing operation may refer to an operation performed on theprojection data to flat the projection data. In some embodiments,smoothed projection data may be generated by subtracting the projectiondata of the model image from the first projection data. In someembodiments, the smoothed projection data may be generated by dividingthe first projection data by the projection data of the model image.After the smoothing process, an interpolation (e.g., a linearinterpolation) may be performed based on the smoothed projection data,and the projection data of the metal image. Interpolated data may begenerated based on the interpolation. Next, an inverse operation may beperformed on the interpolated data. For example, if the smoothedprojection data is generated by a subtraction process, the inverseoperation may include summing the interpolated data and the projectiondata of the model image to obtain a fourth projection data. As anotherexample, if the smoothed projection data is generated by a divisionprocess, the inverse operation may include multiplying the interpolateddata with the projection data of the model image to obtain the fourthprojection data.

In some embodiments, the system may determine whether a weightingoperation may be needed to perform on the first projection data and thefourth projection data. If the answer is “yes,” the system may determinethe weighting coefficients of the fourth projection data according to,for example, the method illustrated in step 806. Based on the weightingoperation, fifth projection data may be generated based on the firstprojection data and the fourth projection data. A further correctedimage may be generated based on the fifth projection data. If the answeris “no,” the further corrected image may be generated based on thefourth projection data. The further corrected image may be generatedaccording to any method disclosed anywhere in the present disclosure.For example, difference between the first projection data and the fourthprojection data or the fifth projection data may be determined. A secondintermediate metal image may be generated based on the difference by abackward projection. The further corrected image may be generated bysubtracting the second intermediate metal image and the metal image fromthe original image.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conducted under the teachingof the present disclosure. For example, a still further correctionsimilar with the correction process (e.g., a smoothing operation, aninterpolation operation, a weighting operation, or the like, or acombination thereof) may be performed on the fifth projection data.However, those variations and modifications may not depart from theprotecting of the present disclosure.

FIG. 9 is a flowchart illustrating a process for determining theweighting coefficients of the projection data of the pre-corrected imageaccording to some embodiments of the present disclosure. In someembodiments, the weighting coefficients of the projection data of thepre-corrected image may be determined based on the shape of theartifacts. For example, during the acquisition process, different metalobjects (e.g., a pin used in a hip, a surgical clip, a dental filling,or the like) may result in metal artifacts with different shapes in theoriginal image. The shape of the artifact(s) may affect the quantity ofthe useful information lost during the pre-correction process.

In step 902, a metal image may be determined. As described in FIG. 7,the metal image may be determined based on a threshold segmentation. Instep 904, a regularity degree (RD) of the metal(s) in the metal imagemay be calculated. Take the metal artifact as an example, if the shapeof the metal object that may give rise to metal artifact(s) is a circle,projection area of the metal artifact resulted from the metal objectwith a regular shape may be fixed along any projection plane. Theprojection area may be defined as an ideal projection area. The idealprojection area may be calculated according to Formula (6) below:

$\begin{matrix}{{S_{IdealPmetal} = {{\frac{2*Space_{pixel}\sqrt{S_{{Imetal}/\pi}}}{Space_{channel}}}*N_{view}}},} & (6)\end{matrix}$

where S_(IdealPmetal) may represent the ideal projection area,S_(lmetai) may represent the number of pixels in the metal image,Space_(pixel) may represent the pixel resolution, Space_(channel) mayrepresent the channel resolution, and N_(view) may represent the numberof the projection angles.

The regularity degree (RD) of the metal artifact(s) may be calculated bycomparing the actual projection area of the metal artifact(s) in themetal image and the ideal projection area. The regularity degree (RD) ofthe metal artifact(s) may be defined by Formula (7):

$\begin{matrix}{{{RD} = \frac{S_{Pmetal}}{S_{{Idea}lPmetal}}},} & (7)\end{matrix}$

where S_(Pmetal) may represent the actual projection area of the metalartifact(s) in the metal image.

As described, the closer to 1 the RD may be, the closer to the idealprojection area the metal artifact(s) may be, it may indicate thatduring the pre-correction (see, e.g., an interpolation as described inFIG. 7), the less the lost useful information may be. Otherwise the morefar away from 1 the RD may be, the more far away from the idealprojection area the metal artifact(s) may be, it may indicate thatduring the pre-correction, the more the lost useful information may be.

In step 906, a determination may be made as to whether the RD exceeds afirst threshold. If the answer is “no,” the process may proceed to step908 to determine a first weighting intensity. If the answer is “yes,”the process may proceed to step 910 to determine whether the RD exceedsa second threshold. In step 910, if the RD exceeds a first threshold butis below a second threshold, the process may proceed to step 912 todetermine a second weighting intensity. If the RD exceeds a secondthreshold, the process may proceed to step 914 to determine a thirdweighting intensity.

In some embodiments, the first threshold and the second threshold may beempirical values, set according to a default setting of the system 100,or set by an operator (e.g., a doctor, an imaging technician, etc.). Forexample, the first threshold may be set as 1-1.8, and the secondthreshold may be set as 1.8-2.2. In some embodiments, the firstweighting intensity, the second weighting intensity, and the thirdweighting intensity may be empirical values, set according to a defaultsetting of the system 100, or set by an operator (e.g., a doctor, animaging technician, etc.). For example, the first weighting intensitymay be set as zero (i.e., no weighting may be performed), the secondweighting intensity may be set as a value between 0 and 0.5, and thethird weighting intensity may be set as a value between 0.5 and 1.

In step 916, the weighting coefficients may be determined based on theone or more weighting intensities. In some embodiments, as the weightingintensity W₀ is determined, the weighting coefficients of the projectiondata of the pre-corrected image may be determined according to Formula(3).

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various variations and modifications may be conducted under the teachingof the present disclosure. For example, one or more optional steps maybe added before step 916, such as in which a third threshold may be setand a fourth weighting intensity may be determined. However, thosevariations and modifications may not depart from the protecting of thepresent disclosure.

FIG. 10 is a flowchart illustrating a process for image correctionaccording to some embodiments of the present disclosure. In step 1002,an error image may be generated based on the original image and thepre-corrected image. In some embodiments, the error image may be definedas:

$\begin{matrix}{{{Ierr} = {{Iori} - {Icorr}}},} & (8)\end{matrix}$

where Iori may represent the original image, Icorr may represent thepre-corrected image, and Ierr may represent the error image.

In step 1004, a plurality of weighting coefficients may be determined. Aweighting coefficient may correspond to a pixel in the error image. Asdescribed in FIG. 7, the pre-corrected image may be generated by apre-correction process. In some embodiments, during the pre-correctionprocess, some additional artifacts may be introduced. Therefore,different weighting coefficients may be determined for different pixelsin the error image. For example, the weighting coefficient of a pixelcorresponding to additional artifact(s) may be smaller than theweighting coefficient of a pixel corresponding to the artifact(s) in theoriginal image. In some embodiments, the weighting coefficient of apixel in the error image may be determined based on the informationentropy of a corresponding pixel in the pre-corrected image. Moredetails may be found in FIG. 11 and the description thereof.

In step 1006, a corrected image may be generated based on the originalimage, the error image, and the plurality of weighting coefficients. Asshown in step 1004, the weighting coefficients of the pixels in theerror image may be determined. To acquire the corrected image, aweighting operation may be performed on the error image and the originalimage according to the determined weighting coefficients.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 11 is a flowchart illustrating a process for determining theweighting coefficient of a pixel in the error image according to someembodiments of the present disclosure. As used herein, an error imagemay refer to an image that may include the difference between theoriginal image and the pre-corrected image. As used herein, an errorimage may refer to an image that may include the difference between theoriginal image and the pre-corrected image. The process for determiningweighting coefficients may be performed by the weighting block 530. Instep 1102, a first neighboring matrix (also referred to as “neighborhoodmatrix”) of a pixel (also referred to as “a first pixel”) in theoriginal image may be determined. For example, there may be N×N pixelsin the original image Iori, and a first neighboring matrix N_(p,k)(Iori)of a pixel (p, k) may be determined.

In step 1104, a second neighboring matrix of a corresponding pixel (alsoreferred to as “a second pixel”) in the error image may be determined.For example, for the error image Ierr, a second neighboring matrixN_(p,k)(Ierr) for a corresponding pixel (p, k) in the error image may bedetermined.

In some embodiments, the size of the first neighboring matrix or thesecond neighboring matrix may affect the computing speed and/or thecorrection effect. For example, the smaller the size of the neighboringmatrix is, the faster the computational speed may be, but the lower thecorrection effect may be. On the contrary, the larger the size of theneighboring matrix is, the better the correction effect may be, but thelower the computational speed may be. In some embodiments, the size ofthe first neighboring matrix or the size of the second neighboringmatrix may be determined based on a feature of the error image (orcalled a “correction effect of the pre-correction process”). Forexample, the better the correction effect of the pre-correction processis, the larger the size of the neighboring matrix may be. That is, themore the artifact(s) is in the error image, the larger size of theneighboring matrix may be. In some embodiments, the size of aneighboring matrix may be set according to a default setting of thesystem 100 (e.g., 9-31 pixels), or determined based on the regularitydegree of an artifact(s). As described in FIG. 9, the RD may bedetermined by comparing the actual projection area of the artifact(s)with the ideal projection area. In some embodiments, the system may setan RD threshold. When the regularity degree (RD) is below the RDthreshold, the size of the neighboring matrix may be set as a smallervalue (e.g., 9 pixels); otherwise, if the regularity degree (RD) exceedsthe RD threshold, the size of the neighboring matrix may be set as alarger value (e.g., 31 pixels). In some embodiments, for a pixel near anedge of an image (e.g., the original image or the error image), acorresponding neighboring matrix may be determined by filling some zeropixels to extend the edge of the image. In some embodiments, the size ofthe first neighboring matrix of the pixel in the original image is equalto that of the second neighboring matrix of the pixel in the errorimage.

In step 1106, a third neighboring matrix of a corresponding pixel (alsoreferred to as “a third pixel”) in the pre-corrected image may bedetermined based on the first neighboring matrix and/or the secondneighboring matrix. As shown in Formula (9), the third neighboringmatrix N_(p,k)(Icw) of the corresponding pixel (p, k) in thepre-corrected image may be generated:

$\begin{matrix}{{{N_{p,k}({Icw})} = {{N_{p,k}({Iori})} - {W_{p,k} \times {N_{p,k}({Ierr})}}}},{{\left( {p,k} \right)\epsilon\; N \times N};{0 \leq W_{p,k} \leq 1}},} & (9)\end{matrix}$

where Icw may represent the pre-corrected image, and W_(p,k) mayrepresent the weighting coefficient of the pixel (p, k) in the errorimage.

In step 1108, information entropy of the corresponding pixel in thepre-corrected image may be calculated based on the third neighboringmatrix. The calculation of the information entropy may be based onFormula (10):

$\begin{matrix}{{{{Entropy}\mspace{14mu}\left( {N_{p,k}({Icw})} \right)} = {- {\sum_{i = 1}^{n}{{P\left( {N_{p,k}({Icw})} \right)}\log_{2}{P\left( {N_{p,k}({Icw})} \right)}}}}},} & (10)\end{matrix}$

where Entropy (N_(p,k)(Icw)) may represent the information entropy ofthe corresponding pixel (p, k) in the pre-corrected image,P(N_(p,k)(Icw)) may represent the prior probability function of thecorresponding pixel in the pre-corrected image, i may represent a pixelin the neighboring matrix, and n may represent the total number of thepixels in the neighboring matrix. In some embodiments, the priorprobability function P(N_(p,k)(Icw)) may be acquired by a histogram. Forexample, the pixel grey values in an image may be analyzed, and thepercentages of the pixel grey values in the image may be determined.

In step 1110, the weighting coefficient of the corresponding pixel inthe error image may be determined based on the calculated informationentropy. In some embodiments, according to Formula (11) below, theweighting coefficient W_(p,k) of the corresponding pixel (p, k) in theerror image may be determined by calculating the minimum informationentropy,

$\begin{matrix}{W_{p,k} = {\arg_{w}{{\min\left( {{Entropy}\mspace{14mu}\left( {N_{p,k}({Icw})} \right)} \right)}.}}} & (11)\end{matrix}$

In some embodiments, the weighting coefficients of other pixels in theerror image may be determined in the same way. After the plurality ofweighting coefficients corresponding to the pixels in the error imageare determined, a weighting operation may be performed on the errorimage and the original image so that a corrected image may be generated.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. For example, step 1102 and step 1104 may bemerged into an independent step in which the first neighboring matrixand the second neighboring matrix may be determined simultaneously.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 12 is a flowchart illustrating a process for image compensationaccording to some embodiments of the present disclosure. Thecompensation operation may be performed by the compensation block 540.In some embodiments, some high frequency information may be lost in thecorrected image. For example, as described in FIG. 10, the correctedimage may be generated by weighting the error image and the originalimage according to a plurality of weighting coefficients. During theweighting process, a mosaic effect may be introduced into the correctedimage. Therefore, a compensation may be performed.

In step 1202, a low frequency part of the corrected image may besegmented. In some embodiments, the artifact(s) may be low frequencycomponent(s) in an image, and edge information of an organ may be highfrequency component in the image. In some embodiments, a low frequencypart of the corrected image may be acquired by performing a low passfiltering on the corrected image. The low pass filter may include, forexample, a Gaussian low pass filter, a Butterworth low pass filter, aChebyshev low pass filter, or the like, or a combination thereof. As anexample, the low pass filtering may be performed by a Gaussian low passfilter G(σ) according to Formula (12):

$\begin{matrix}{{{G(\sigma)} = {{e^{\frac{- x^{2}}{2}}/2}\sigma^{2}}},} & (12)\end{matrix}$

where σ is an empirical value, and x may represent the distance. Forinstance, the range of σ may be 1.2-1.3.

The low frequency part of the corrected image may be segmented accordingto Formula (13):

$\begin{matrix}{{{{Icor}r2L{ow}} = {{Icor}r2*{G(\sigma)}}},} & (13)\end{matrix}$

where Icorr2 may represent the corrected image, and Icorr2Low mayrepresent the low frequency part of the corrected image. In someembodiments, a high frequency part of the corrected image may beobtained by subtracting the low frequency part from the corrected image.

In step 1204, a high frequency part of the original image may besegmented. Firstly, a low frequency part of the original image may besegmented according to Formula (14):

$\begin{matrix}{{{IoriLow} = {{Iori}*{G(\sigma)}}},} & (14)\end{matrix}$

where Iori may represent the original image, and IoriLow may representthe low frequency part of the original image. Secondly, the highfrequency part of the original image may be obtained according toFormula (15):

$\begin{matrix}{{{IoriHigh} = {{Iori} - {IoriLow}}},} & (15)\end{matrix}$

where IoriHigh may represent the high frequency part of the originalimage.

In step 1206, the high frequency part of the original image, the highfrequency part of the corrected image, and the low frequency part of thecorrected image may be fused. In some embodiments, a final correctedimage may be generated based on the compensation. In some embodiments,the high frequency part of the original image may include noise(s). Inorder to reduce the noise(s), a weighting mask may be generated withrespect to the high frequency part of the original image. The weightingmask may include weighting coefficients for various pixels. For example,for a pixel close to a metal artifact, a relatively large weightingcoefficient may be chosen; for a pixel distant to a metal artifact, arelatively small weighting coefficient may be chosen.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations or modifications may be made under the teachings ofthe present disclosure. For example, in order to improve computationalspeed, one or more of the original image, the pre-corrected image, andthe error image may be compressed. However, those variations andmodifications do not depart from the scope of the present disclosure.

FIG. 13-A is a block diagram illustrating an architecture of acorrection unit according to some embodiments of the present disclosure.The correction unit 330 may include a determination block 1301, adetection block 1302, an image creation block 1303, and a processingblock 1304. In some embodiments, at least two of the blocks may beconnected with each other via a wired connection (e.g., a metal cable,an optical cable, a hybrid cable, or the like, or any combinationthereof) or a wireless connection (e.g., a Local Area Network (LAN), aWide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or any combination thereof). In someembodiments, the blocks may be connected with each other through amedium. The medium may include a visible medium or an invisible medium(e.g., radio, optical, sonic, electromagnetic induction, etc.).

The determination block 1301 may determine a region of interest (ROI) inan original image. As used herein, the original image may refer to animage that is reconstructed based on the acquired signal. In someembodiments, the original image may be reconstructed by thereconstruction unit 320, or retrieved from the storage unit 340, anexternal resource (e.g., a hard disk, a floppy disk, a wirelessterminal, or the like, or a combination thereof), or any storagedisclosed anywhere in the present disclosure. In some embodiments, theROI may be determined from a plurality of candidate regions of interestaccording to a criterion (more details may be found in FIG. 14 and thedescription thereof).

In some embodiments, the determination block 1301 may determine an imagerelating to the ROI from the original image. As used herein, an imagerelating to the ROI may refer to an image that is extracted from theoriginal image. In some embodiments, the extraction may be performed byapplying a mask to other region(s) besides the ROI in the originalimage, or cutting out the ROI from the original image. As used herein, amask may refer to an object that may be used to cover a specific regionin an image. In some embodiments, for a digital image, the mask mayinclude a matrix (e.g., a two-dimensional array), a binary image inwhich value of a pixel may be 0 or 1, or the like, or any combinationthereof.

The detection block 1302 may detect artifacts in the image relating tothe ROI. In some embodiments, the artifact may include a metal artifact,a streak artifact, or the like, or a combination thereof. As usedherein, the metal artifact may result from a metal object (e.g., a pinused in a hip, a surgical clip, a dental filling, or the like, or acombination thereof). The streak artifact may result fromunder-sampling, photon starvation, motion, beam hardening, Comptonscatter, or the like, or a combination thereof, during the scanning. Insome embodiments, during the detection of the artifacts, the imagerelating to the ROI may be processed (e.g., rotated, smoothed, filtered,or the like, or a combination thereof). In some embodiments, thedetected artifacts may be further processed, for example, the artifactsmay be marked in the image relating to the ROI or extracted from theimage relating to the ROI.

The image creation block 1303 may generate an artifact image (e.g., animage including streak artifact of an original image, or referred to asa streak artifact image for brevity). In some embodiments, the artifactimage may be generated by filtering the artifacts from the imagerelating to the ROI. In some embodiments, the artifact image may befurther processed. For example, a compensation may be performed on theartifact image.

The processing block 1304 may process the original image, the imagerelating to the ROI, or the artifact image. In some embodiments, theoriginal image may be corrected based on the image relating to the ROIand the artifact image.

It should be noted that the above description of the correction unit 330is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations or modifications may be madeunder the teachings of the present disclosure. For example, thedetermination block 1301 and the detection block 1302 may be integratedin an independent unit or block used to determine the ROI and detect theartifacts. As another example, the correction unit 330 may furtherinclude a storage block (not shown). The storage block may be used tostore the acquired image and/or any intermediate data generated duringany process performed by any block in the correction unit 330. As afurther example, at least some of the blocks in the correction unit 330may each include a storage block, or at least two of the blocks mayshare a common storage block. However, those variations andmodifications do not depart from the scope of the present disclosure.

FIG. 13-B is a flowchart illustrating a process for correcting an imageaccording to some embodiments of the present disclosure. In step 1310,an original image may be acquired. The original image may bereconstructed by the reconstruction unit 320, or retrieved from thestorage unit 340, an external resource (e.g., a hard disk, a floppydisk, a wireless terminal, or the like, or a combination thereof), orany storage disclosed anywhere in the present disclosure.

In step 1312, an image relating to a region of interest (ROI) may bedetermined. The determination may be performed by the determinationblock 1301. As used herein, an image relating to an ROI may refer to animage that is extracted from the original image. The extraction may beperformed by applying a mask to other region(s) besides the ROI in theoriginal image, or cutting out the ROI from the original image. In someembodiments, the ROI may be determined from candidate regions ofinterest according to a criterion (more details may be found in FIG. 14and the description thereof).

In step 1314, an artifact in the image relating to the ROI may bedetected. The detection may be performed by the detection block 1302. Insome embodiments, the artifact may be a streak artifact. In someembodiments, during the detection, an angle range may be determined, aprocessing angle may be selected from the angle range, and the imagerelating to the ROI may be rotated based on the processing angle. Moredetails may be found in FIG. 15 and the description thereof.

In step 1316, an artifact image may be generated based on the detectedartifact. In some embodiments, the artifact image may be generated byfiltering the artifacts from the image relating to the ROI. In step1318, the original image may be corrected based on the artifact image.Merely by way of example, the original image may be corrected bysubtracting the artifact image from the original image (more details maybe found in FIG. 16 and the description thereof).

It should be noted that the above description of the process forcorrecting an image is merely provided for the purposes of illustration,and not intended to limit the scope of the present disclosure. Forpersons having ordinary skills in the art, multiple variations ormodifications may be made under the teachings of the present disclosure.For example, in some embodiments, a determination may be made as towhether the detected artifact may be a potential tissue. Furthermore, acompensation may be performed if the detected artifact is determined tobe a potential tissue. However, those variations and modifications donot depart from the scope of the present disclosure.

FIG. 14 is a flowchart illustrating a process for determining an imagerelating to a region of interest (ROI) according to some embodiments ofthe present disclosure. The process for determining the image relatingto the ROI may be performed by the determination block 1301. In step1402, an original image may be acquired. In step 1404, the originalimage may be segmented into a plurality of portions. For example, for anoriginal image regarding the head of a subject, the plurality ofportions may include a portion including air, a portion including atissue, or the like, or a combination thereof. In some embodiments, asegmentation method may be used. The segmentation method may include athresholding method, a clustering method, a compression-based method, ahistogram-based method, an edge detection method, or the like, or anycombination thereof.

In step 1405, candidate regions of interest in the original image may bedetermined. As used herein, candidate regions of interest may refer toone or more regions in the original image from which the ROI may beselected. In some embodiments, the candidate regions of interest may bedetermined according to a first criterion. The first criterion may beset according to a default setting of the system 100, selected based onstatistical information, or set by an operator (e.g., a doctor, animaging technician, etc.). In some embodiments, different criteria maybe set for different images (e.g., an image regarding the head of asubject, an image regarding a lung of a subject, or the like). Merely byway of example, for an image regarding the head of a subject, thecandidate regions of interest may be determined based on the centroidpositions of the candidate regions of interest, and/or the sizes of thecandidate regions of interest, etc. (more details may be found in FIG.16-A and the description thereof).

In step 1406, a region of interest (ROI) may be determined from thecandidate regions of interest based on the plurality of portions (e.g.,a portion including air, a portion including a tissue, or the like)segmented in step 1404. In some embodiments, the determination may beperformed according to a second criterion. The second criterion may beset according to a default setting of the system, selected based onstatistical information, or set by an operator (e.g., a doctor, animaging technician, etc.). In some embodiments, a specific candidateregion of interest that may satisfy a preset condition may be selectedas the ROI. The preset condition may be a threshold regarding thepercentage of a portion in the image. Different preset conditions may beset for different images (e.g., an image regarding the head of asubject, an image regarding a lung of the subject, or the like). Forinstance, for an image regarding the head of a subject, the presetcondition may be a threshold regarding the percentage of the tissue inthe head and/or a threshold regarding percentage of the air in the head.More details may be found in FIG. 16 and the description thereof.

In step 1408, an image relating to the ROI may be obtained. As mentionedin FIG. 13-B, the image relating to the ROI may be further processed. Insome embodiments, an artifact may be detected in the determined imagerelating to the ROI and an artifact image may be further generated.

It should be noted that the above description of the process fordetermining an image relating to a region of interest (ROI) is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations or modifications may be made under theteachings of the present disclosure. For example, in step 1402, anoptimizing operation (e.g., denoising, etc.) may be performed on theoriginal image. However, those variations and modifications do notdepart from the scope of the present disclosure.

FIG. 15 is a flowchart illustrating a process for generating an artifactimage according to some embodiments of the present disclosure. Theprocess for generating an artifact image may be performed by the imagecreation block 1303. In step 1502, an angle range may be determined. Theangle range may be determined based on the size of the region in whichthe artifacts occur. In some embodiments, the angle range may include acombination of a plurality of single angles (e.g., 30 degrees, 40degrees, 60 degrees, etc.), or a continuous angle range with anincremental step (e.g., between 30 degrees and 90 degrees withone-degree step). In some embodiments, the angle range may be determinedbased on a default setting of the system 100, or may be set by anoperator (e.g., a doctor). In some embodiments, different angle rangesmay be determined for different images. For example, for an imageregarding the head of a subject, the angle range may be determined basedon the central angle of the head.

In step 1504, a processing angle may be selected from the angle range.For example, for an angle range between 30 degrees and 90 degrees with aone-degree step, a processing angle of 30 degrees may be selected. Instep 1506, the image relating to the ROI may be rotated based on theprocessing angle. The directions of the artifacts to be detected in animage may be various. During the detection, the artifacts in an imagemay be rotated to a same direction (e.g., the X-axis). In someembodiments, further processing (e.g., smoothing, filtering, or thelike, or a combination thereof) may be performed by rotating the imageincluding artifacts to the directions of the artifacts. For example, ifthe processing angle selected in step 1504 is 31 degrees, the imagerelating to the ROI may be rotated from 0 degree to −31 degrees, and theartifacts at 31 degrees may be rotated to the X-axis.

In step 1508, an intermediate image may be generated based on therotated image. As used herein, an intermediate image may refer to animage that may be generated by processing the rotated image. In someembodiments, the intermediate image may be generated by subtracting asmoothed image from the rotated image relating to the ROI. As usedherein, the smoothed image may be generated by smoothing the rotatedimage relating to the ROI. More details regarding the smoothed image maybe found in FIG. 16-A and FIG. 16-B, and the description thereof.

In step 1510, a determination may be made as to whether an artifact isdetected in the intermediate image. Merely by way of example, theintermediate image may be processed, and whether an artifact may bedetected may be determined by analyzing gradient direction of theprocessed intermediate image. More details regarding the determinationmay be found in FIG. 16-B and the description thereof. If the answer is“yes,” the process may proceed to step 1512 to generate an artifactimage based on the artifact. The artifact image may be further used tocorrect the original image. If the answer is “no,” the process mayreturn to step 1504 to select a new processing angle from the anglerange. A new intermediate image may be generated based on the newprocessing angle by repeating step 1504 through step 1508.

It should be noted that the above description of the process forgenerating an artifact image is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations or modifications may be made under the teachings of thepresent disclosure. For example, step 1504 and step 1506 may beperformed simultaneously or successively. However, those variations andmodifications do not depart from the scope of the present disclosure.

FIG. 16-A through FIG. 16-C illustrate an exemplary process forgenerating a corrected image according to some embodiments of thepresent disclosure. In step 1602, an original image may be acquired. Theoriginal image may be a CT image acquired from a portion (e.g., thehead) of a subject.

In step 1604, the original image may be segmented into a plurality ofportions according to a segmentation method. In some embodiments, thesegmentation method may include a thresholding method, a clusteringmethod, a compression-based method, a histogram-based method, an edgedetection method, or the like, or any combination thereof. In someembodiments, the segmentation may be performed according to athresholding method. For example, one or more thresholds regarding CTvalue may be set. Merely by way of example, the original image may beacquired from the head of a subject. According to the thresholdsregarding CT value, the original image may be segmented into a portionincluding air, a portion including a tissue, or the like, or acombination thereof.

In some embodiments, a set of pixels whose CT values are between 900 Huand 1100 Hu may be deemed as the portion including a tissue; a set ofpixels whose CT values are between 0 Hu and 500 Hu may be deemed as theportion including air.

In step 1606, candidate regions of interest in the original image may bedetermined. As used herein, candidate regions of interest may refer toone or more regions in the original image from which the ROI may beselected. In some embodiments, the candidate regions of interest may bedetermined according to a first criterion. The first criterion may beset according to a default setting of the system 100, selected based onstatistical information, or set by an operator (e.g., a doctor, animaging technician, etc.). In some embodiments, different criteria maybe set for different images (e.g., an image regarding the head of asubject, an image regarding a lung of a subject, or the like). Forexample, for an image regarding the head of a subject, the candidateregions of interest may satisfy the following requirements: thecentroids of the candidate regions of interest may be below the centroidof the head, and x directions of the centroids of the candidate regionsof interest may not deviate from that of the centroid of the head toofar (e.g., less than 20%); for a specific candidate region, the centroidis within the candidate region of interest; and the sizes of thecandidate regions of interest may be not too small. For example, for animage regarding the head of a subject, the candidate region of interestmay be 1% and 30% of the size of the head.

In step 1608, a region of interest (ROI) may be determined from thecandidate regions of interest based on the plurality of portions. Insome embodiments, the determination may be performed according to asecond criterion. The second criterion may be set according to a defaultsetting of the system, selected based on statistical information, or setby an operator (e.g., a doctor, an imaging technician, etc.). In someembodiments, a specific candidate region of interest that may satisfy apreset condition may be selected as the ROI. Different preset conditionsmay be set for different images (e.g., an image regarding the head of asubject, an image regarding a lung of a subject, or the like). In someembodiments, for an image regarding the head of a subject, the presetcondition may be a threshold regarding percentage of the tissue in thehead and/or a threshold regarding percentage of the air in the head. Forexample, for a specific candidate region of interest, the percentage ofthe issue in the head is less than 30%, and the percentage of the air inthe head is larger than 1%, it may be determined as the ROI.

In step 1610, an image relating to the ROI may be obtained. As usedherein, an image relating to the ROI may refer to an image that isextracted from the original image. In some embodiments, the extractionmay be performed by applying a mask to other region(s) besides the ROIin the original image, or cutting out the ROI from the original image.As used herein, a mask may refer to an object that may be used to covera specific region in an image. In some embodiments, for a digital image,the mask may include a matrix (e.g., a two-dimensional array), a binaryimage in which value of a pixel may be 0 or 1, or the like, or anycombination thereof.

In step 1612, an angle range may be determined. The angle range may bedetermined based on the size of the region in which the artifacts occur.In some embodiments, the angle range may be determined based on anextension of a central angle. For example, for an image regarding thehead of a subject, the central angle may be the central angle of thehead. In some embodiments, the angle range may be determined accordingto Formula (16) below:

$\begin{matrix}{{R = \left\lbrack {{{DCenter} - {DExtend}},{{DCenter} + {DExtend}}} \right\rbrack},{{iD} = 1},} & (16)\end{matrix}$

where R may represent the angle range, DCenter may represent the centralangle, DExtend may represent the extension of the central angle, and iDmay represent the incremental step.

In step 1614, a processing angle may be selected from the angle range.For example, for an angle range from 30 degrees to 90 degrees, aprocessing angle of 30 degrees may be selected. In step 1616, the imagerelating to the ROI may be rotated based on the processing angle. Basedon the rotation of the image relating to the ROI, the direction(s) ofthe artifact(s) to be detected may be adjusted to the X-axis. After theimage relating to the ROI is rotated, the process may follow at leastsome steps starting from node A 1618 as illustrated in FIG. 16-B.

FIG. 16-B illustrates an exemplary process for generating a streakartifact image according to some embodiments of the present disclosure.In step 1620, the rotated image relating to the ROI may be smoothed. Thesmoothing algorithm may include a cubical smoothing algorithm withfive-point approximation, a linear smoothing method, an interpolationmethod, or the like, or any combination thereof. In step 1626, asmoothed image may be generated based on the rotated image relating tothe ROI. In step 1628, a subtracted image may be generated. As usedherein, a subtracted image may refer to an image generated bysubtracting the smoothed image from the rotated image relating to theROI.

In step 1630, a determination may be made as to whether a streakartifact is detected in the subtracted image. If the answer is “no,” theprocess may follow at least some steps staring from node C 1631 asillustrated in FIG. 16-C. If the answer is “yes,” the process mayproceed to step 1632 to determine a region including the streakartifact.

In some embodiments, in step 1630, during the detection of the streakartifact, the subtracted image may be processed. A gradient direction ofthe subtracted image may be calculated according to Formula (17) below:

$\begin{matrix}{{{Gdir} = {\arctan\left( \frac{\frac{\partial{Irs}}{\partial y}}{\frac{\partial{Irs}}{\partial x}} \right)}},} & (17)\end{matrix}$

where Gdir may represent the gradient direction of the subtracted image,and Irs may represent the subtracted image. In some embodiments, thesubtracted image may be dispersed by a difference operator, e.g., aSobel operator. A plurality of rows (e.g., M rows) of the subtractedimage may be determined, and the row may include a plurality of pixels.The numbers of pixels in the plurality of rows may be different. Forexample, for row m, there may be a pixels, for row n, there may be bpixels. Furthermore, the sum of sine values of the gradient directionsof the row in the subtracted image may be calculated according toFormula (18) below:

$\begin{matrix}{{{{Sum}\mspace{14mu}(m)} = {\sum_{1}^{N}{\sin\left( {Gdi{r(m)}} \right)}}},} & (18)\end{matrix}$

where m may represent a row of the subtracted image, Sum(m) mayrepresent the sum of sine values of the gradient directions of thepixels in the row m, sin(Gdir(m)) may represent the sine value of thegradient direction of row m in the subtracted image, and N may representtotal number of pixels in the row m.

In some embodiments, a statistical curve regarding the sum of sinevalues of the gradient directions may be obtained. For the statisticalcurve, the x-axis may represent the row (e.g., row m, row n, or thelike) of the subtracted image, the y-axis may represent the sum of sinevalues of the gradient directions of the row. Peaks and valleys may beextracted on the statistical curve. A peak or a valley may correspond toa row. A streak artifact may be detected in the subtracted image basedon the peaks and valleys. For example, for an image regarding the headof a subject, if the peaks and the valleys satisfy some presetconditions, it may indicate that the positions in the subtracted imagecorresponding to the peaks and valleys may be positions corresponding tostreak artifacts. In some embodiments, the preset conditions may beprovided by at least two thresholds, for example, T1 and T2. T1 maystand for a threshold with respect to the value of a peak or the valueof a valley (i.e., T1 stands for a threshold with respect to the sum ofsine values of the gradient directions of the row). T2 may stand for athreshold with respect to the distance between one pair of a valley anda peak. In some embodiments, the threshold(s) T1 may be different fordifferent rows in the subtracted image. For example, the values of T1may vary as a threshold curve. For the threshold curve, the x-axis mayrepresent the row in the subtracted image, the y-axis may represent thevalue of the peak or the value of the valley (also referred to as“amplitude”). In some embodiments, T1 and T2 may be set based on adefault setting of the system 100, empirical values, or by an operator(e.g., a doctor). In some embodiments, T1 and T2 may be set according tothe resolution of the subtracted image. Merely by way of example, for arow in an image regarding the head of a subject with a 512×512 matrixand 230 FOV (field of view), in this situation, the row includes 180pixels, T1 may be set as 50. As used herein, the threshold “50” may bereferred to as a “basic threshold”. For a row including N pixels, thevalue of T1 may be (N×50)/180. T2 may be set as a distance value betweena pair of peak and valley which corresponds to 3-8 pixels (e.g., 4pixels) between the two corresponding rows in the subtracted image. Thepositions of the peak and the valley that satisfy the preset conditionsmay be recorded as a start position and an end position of the streakartifact, respectively.

In step 1632, a region including the streak artifact may be determinedbased on the detected streak artifact. In some embodiments, the regionmay be determined based on an extension of the start position and theend position of the streak artifact. For example, in some embodiments,several pixels (e.g., 2-3 pixels) may be extended along the startposition and the end position of the streak artifact. The regionincluding the streak artifact may be determined according to Formula(19) and Formula (20) below:

$\begin{matrix}{{{Rstart} = {{{Rstart}\; 0} - {Rn}}},} & (19) \\{{{Rend} = {{Rend0} + {Rn}}},} & (20)\end{matrix}$

where Rstart may represent the start position of the region includingthe streak artifact, Rend may represent the end position of the regionincluding the streak artifact, Rstart0 may represent the start positionof the detected streak artifact, Rend0 may represent the end position ofthe detected streak artifact, and Rn may represent the extension pixelsalong Rstart0 and Rend0. It should be noted that the extension shouldnot exceed the edge of the subtracted image.

In step 1634, the region including the streak artifact may be filtered.In some embodiments, the filtering may be performed by a comb filter. Instep 1636, a streak artifact image may be generated based on thefiltering in step 1634. As used herein, a streak artifact image mayrefer to an image only including the streak artifact(s). After thestreak artifact image is generated, the process may follow at least somesteps starting from node D 1638 as illustrated in FIG. 16-C.

FIG. 16-C illustrates an exemplary process for generating a correctedimage according to some embodiments of the present disclosure. In step1640, a compensation may be performed on the streak artifact image. Insome embodiments, a “fake artifact” may appear in the streak artifactimage. As used herein, a “fake artifact” may refer to a potential tissue(e.g., a blood vessel, a tissue of a head, or the like, etc.) that maybe identified as a streak artifact. In order to remove or reduce the“fake artifact” in the streak artifact image, a compensation may beperformed on the streak artifact image. Merely by way of example, theremay be a plurality of streaks in the streak artifact image. CT values ofthe streaks may be computed and analyzed, and a streak peak and a streakvalley may be determined. In some embodiments, whether a compensationmay be performed may be determined. For example, if a streak valley isdetermined and the position of the streak valley is closer to the skullbase than that of the streak peak, it may indicate that there may be apotential tissue in the streak artifact image and a compensation may beneeded. In some embodiments, the compensation may be performed accordingto Formula (21) below:

$\begin{matrix}{{{{Ire}{C\left( {{iStre{{akPeak}:{end}}},:} \right)}} = {{Ir}{e\left( {iStre{{akPeak}:{{end}:}}} \right)}*{Weighting}}},} & (21)\end{matrix}$

where IreC(iStreakPeak:end,:) may represent the compensated part of thestreak artifact image, Weighting may represent weighting coefficient ofthe compensation, iStreakPeak may represent the position of the streakpeak, end may represent the end position of the streaks in the streakartifact image, and Ire(iStreakPeak:end,:) may represent the part of thestreak artifact image to be compensated.

In step 1642, the compensated streak artifact image may be re-rotatedbased on the processing angle in connection with the rotationillustrated in step 1616. In step 1644, a preliminarily corrected imagemay be generated based on the image relating to the ROI and there-rotated streak artifact image. According to some embodiments of thepresent disclosure, the preliminarily corrected image may be generatedby subtracting the re-rotated streak artifact image from the imagerelating to the ROI.

In step 1646, the system may determine whether all the processing angleswithin the angle range are selected. If the answer is “yes,” the processmay proceed to step 1648 to generate a corrected image. If the answer is“no,” the process may proceed to node B 1624 to return back to step1614, i.e., a new processing angle may be selected from the angle rangeand a next correction process may be performed by repeating step 1614through step 1644. In some embodiments, the next correction process maybe performed based on the preliminarily corrected image generated instep 1644. For example, the image relating to the ROI in the nextcorrection process may be updated by the preliminary corrected imagegenerated in step 1644.

In step 1648, a corrected image may be generated. As used herein, acorrected image may refer to an image generated by subtracting thestreak artifact image from the original image. As illustrated in step1644, a preliminarily corrected image may be generated. In someembodiments, the corrected image may be generated by supplementing otherregion(s) besides the ROI to the preliminarily corrected image. Forexample, the mask that may be used to cover other region(s) besides theROI may be removed. As another example, other region(s) besides the ROIthat may be cut from the original image may be added. In someembodiments, a streak artifact image including all the streak artifactsmay be generated based on the preliminarily corrected image. Thecorrected image may be generated by subtracting the streak artifactimage including all the streak artifacts from the original image.

Furthermore, in some embodiments, before generating the corrected image,the streak artifact image including all the streak artifacts may besmoothed. In some embodiments, the smoothing may be performed infrequency domain or in time domain. The smoothing algorithm may includea Gauss filtering algorithm, a Median filtering algorithm, or the like,or any combination thereof. In some embodiments, the smoothing may beperformed based on the resolution of the streak artifact image. Merelyby way of example, a streak artifact image with high resolution (e.g.,pixel spacing <0.7) may be smoothed.

It should be noted that the above description of the process forgenerating a corrected image is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations or modifications may be made under the teachings of thepresent disclosure. For example, in some embodiments, before thesegmentation of the original image in step 1604, the original image maybe compressed according to a compression ratio (e.g., 8:1). Thedetermined ROI determined may be marked in the original image accordingto the compression ratio. As another example, in some embodiments, theresult of the current correction process may be provided as a feedbackfor the next correction process. In general, for a scanning sequence, aplurality of images may be reconstructed. The plurality of images may becorrected in order. If no ROI is determined in the current correctionprocess, it may indicate that there may be no ROI(s) in the image(s)that may be processed later. However, those variations and modificationsdo not depart from the scope of the present disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “module,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method for image correction implemented on acomputing device including at least one processor and at least onestorage device, the method comprising: receiving an original image;obtaining an image relating to a region of interest (ROI); detecting anartifact in the image relating to the ROI, wherein the artifact is astreak artifact; generating an artifact image based on the artifact; andcorrecting the original image based on the artifact image.
 2. The methodof claim 1, wherein the obtaining the image relating to the ROIincludes: segmenting the original image into a plurality of portions,the plurality of portions at least comprising an air portion and atissue portion; determining candidate ROIs in the original image; anddetermining the ROI from the candidate ROIs based on the plurality ofportions.
 3. The method of claim 2, wherein a percentage of the tissueportion in the ROI is below a threshold.
 4. The method of claim 1,wherein the detecting the artifact in the image relating to the ROIincludes: determining an angle range; selecting a processing angle fromthe angle range; rotating the image relating to the ROI by theprocessing angle; and detecting the artifact based on the rotated image.5. The method of claim 4, wherein the detecting the artifact based onthe rotated image includes: obtaining a smoothed image by smoothing therotated image; generating a subtracted image based on the original imageand the smoothed image; and detecting the artifact in the subtractedimage.
 6. The method of claim 5, wherein the detecting the artifact inthe subtracted image includes: determining a plurality of rows in thesubtracted image; for each of the plurality of rows, determining a sumof sine values of gradient directions of pixels in the row; anddetecting the artifact in the subtracted image based on sums of sinevalues of gradient directions corresponding to the plurality of rows. 7.The method of claim 6, wherein the detecting the artifact in thesubtracted image based on the sums of sine values of gradient directionscorresponding to the plurality of rows includes: determining astatistical curve of the sums of sine values of gradient directions,wherein an x-axis of the statistical curve represents the plurality ofrows in the subtracted image and a y-axis of the statistical curverepresents the sums of sine values of gradient directions; and peaks orvalleys on the statistical curve correspond to the plurality of rows;and detecting the artifact in the subtracted image based on the peaksand valleys on the statistical curve.
 8. The method of claim 7, whereinthe detecting the artifact in the subtracted image based on the peaksand valleys on the statistical curve includes: detecting the artifact inthe subtracted image based on a first threshold associated with valuesof the peaks or the valleys and a second threshold associated with adistance between a pair of valley and a peak.
 9. The method of claim 1,wherein the generating the artifact image based on the artifactincludes: determining a region including the artifact based on anextension of a start position and an end position of the artifact; anddetermining the artifact image by filtering the region including theartifact.
 10. The method of claim 1, wherein the method furtherincludes: in response to determining that the detected artifact is apotential tissue; and performing a compensation to the artifact image.11. A system, comprising: at least one storage device including a set ofinstructions for image correction; at least one processor incommunication with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to cause the system to perform operations including:receiving an original image obtaining an image relating to a region ofinterest (ROI); detecting an artifact in the image relating to the ROI,wherein the artifact is a streak artifact; generating an artifact imagebased on the artifact; and correcting the original image based on theartifact image.
 12. The system of claim 11, wherein the obtaining theimage relating to the ROI includes: segmenting the original image into aplurality of portions, the plurality of portions at least comprising anair portion and a tissue portion; determining candidate ROIs in theoriginal image; and determining the ROI from the candidate ROIs based onthe plurality of portions.
 13. The system of claim 12, wherein apercentage of the tissue portion in the ROI is below a threshold. 14.The system of claim 11, wherein the detecting the artifact in the imagerelating to the ROI includes: determining an angle range; selecting aprocessing angle from the angle range; rotating the image relating tothe ROI by the processing angle; and detecting the artifact based on therotated image.
 15. The system of claim 14, wherein the detecting theartifact based on the rotated image includes: obtaining a smoothed imageby smoothing the rotated image; generating a subtracted image based onthe original image and the smoothed image; and detecting the artifact inthe subtracted image.
 16. The system of claim 15, wherein the detectingthe artifact in the subtracted image includes: determining a pluralityof rows in the subtracted image; for each of the plurality of rows,determining a sum of sine values of gradient directions of pixels in therow; and detecting the artifact in the subtracted image based on sums ofsine values of gradient directions corresponding to the plurality ofrows.
 17. The system of claim 16, wherein the detecting the artifact inthe subtracted image based on the sums of sine values of gradientdirections corresponding to the plurality of rows includes: determininga statistical curve of the sums of sine values of gradient directions,wherein an x-axis of the statistical curve represents the plurality ofrows in the subtracted image and a y-axis of the statistical curverepresents the sums of sine values of gradient directions; and peaks orvalleys on the statistical curve correspond to the plurality of rows;and detecting the artifact in the subtracted image based on the peaksand valleys on the statistical curve.
 18. The system of claim 17,wherein the detecting the artifact in the subtracted image based on thepeaks and valleys on the statistical curve includes: detecting theartifact in the subtracted image based on a first threshold associatedwith values of the peaks or the valleys and a second thresholdassociated with a distance between a pair of valley and a peak.
 19. Thesystem of claim 11, wherein the generating the artifact image based onthe artifact includes: determining a region including the artifact basedon an extension of a start position and an end position of the artifact;and determining the artifact image by filtering the region including theartifact.
 20. A non-transitory computer readable medium, comprisingexecutable instructions that, when executed by at least one processor,direct the at least one processor to perform a method, the methodcomprising: receiving an original image obtaining an image relating to aregion of interest (ROI); detecting an artifact in the image relating tothe ROI, wherein the artifact is a streak artifact; generating anartifact image based on the artifact; and correcting the original imagebased on the artifact image.